scholarly journals Event-Tree Based Sequence Mining Using LSTM Deep-Learning Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
János Abonyi ◽  
Richárd Károly ◽  
Gyula Dörgö
Keyword(s):  
Transition Probabilities ◽  
Probability Model ◽  
Input Sequence ◽  
Process Variable ◽  
Event Tree ◽  
Online Application ◽  
Original Dataset ◽  
Subsequent Event ◽  
System States ◽  
And Performance

During the operation of modern technical systems, the use of the LSTM model for the prediction of process variable values and system states is commonly widespread. The goal of this paper is to expand the application of the LSTM-based models upon obtaining information based on prediction. In this method, by predicting transition probabilities, the output layer is interpreted as a probability model by creating a prediction tree of sequences instead of just a single sequence. By further analyzing the prediction tree, we can take risk considerations into account, extract more complex prediction, and analyze what event trees are yielded from different input sequences, that is, with a given state or input sequence, the upcoming events and the probability of their occurrence are considered. In the case of online application, by utilizing a series of input events and the probability trees, it is possible to predetermine subsequent event sequences. The applicability and performance of the approach are demonstrated via a dataset in which the occurrence of events is predetermined, and further datasets are generated with a higher-order decision tree-based model. The case studies simply and effectively validate the performance of the created tool as the structure of the generated tree, and the determined probabilities reflect the original dataset.

scholarly journals Massively multi-user online platform for large-scale applications

2021 ◽  
Author(s):  
Allen Yen-Cheng Yu
Keyword(s):  
Grid Computing ◽  
High Performance ◽  
Large Scale ◽  
Distributed Applications ◽  
Service Architecture ◽  
Online Application ◽  
Online Platform ◽  
Massively Multiplayer Online Game ◽  
And Performance

Many large-scale online applications enable thousands of users to access their services simultaneously. However, the overall service quality of an online application usually degrades when the number of users increases because, traditionally, centralized server architecture does not scale well. In order to provide better Quality of Service (QoS), service architecture such as Grid computing can be used. This type of architecture offers service scalability by utilizing heterogeneous hardware resources. In this thesis, a novel design of Grid computing middleware, Massively Multi-user Online Platform (MMOP), which integrates the Peer-to-Peer (P2P) structured overlays, is proposed. The objectives of this proposed design are to offer scalability and system design flexibility, simplify development processes of distributed applications, and improve QoS by following specified policy rules. A Massively Multiplayer Online Game (MMOG) has been created to validate the functionality and performance of MMOP. The simulation results have demonstrated that MMOP is a high performance and scalable servicing and computing middleware.

scholarly journals Software Fault Estimation Tool Based on Object-Oriented Metrics

2021 ◽  
pp. 63-69
Author(s):  
Atica M. Altaie ◽  
Asmaa Yaseen Hamo ◽  
Rasha Gh. Alsarraj
Keyword(s):  
Fault Estimation ◽  
Log Transformation ◽  
Software Metric ◽  
A Value ◽  
Original Dataset ◽  
Proposed Model ◽  
E Learning ◽  
And Performance ◽  
Number Of Classes ◽  
Object Oriented Metrics

A fault is an error that has effects on system behaviour. A software metric is a value that represents the degree to which software processes work properly and where faults are more probable to occur. In this research, we study the effects of removing redundancy and log transformation based on threshold values for identifying faults-prone classes of software. The study also contains a comparison of the metric values of an original dataset with those after removing redundancy and log transformation. E-learning and system dataset were taken as case studies. The fault ratio ranged from 1%-31% and 0%-10% for the original dataset and 1%-10% and 0%-4% after removing redundancy and log transformation, respectively. These results impacted directly the number of classes detected, which ranged between 1-20 and 1-7 for the original dataset and 1-7 and 0-3) after removing redundancy and log transformation. The Skewness of the dataset was deceased after applying the proposed model. The classified faulty classes need more attention in the next versions in order to reduce the ratio of faults or to do refactoring to increase the quality and performance of the current version of the software.

scholarly journals Massively multi-user online platform for large-scale applications

2021 ◽  
Author(s):  
Allen Yen-Cheng Yu
Keyword(s):  
Grid Computing ◽  
High Performance ◽  
Large Scale ◽  
Distributed Applications ◽  
Service Architecture ◽  
Online Application ◽  
Online Platform ◽  
Massively Multiplayer Online Game ◽  
And Performance

Many large-scale online applications enable thousands of users to access their services simultaneously. However, the overall service quality of an online application usually degrades when the number of users increases because, traditionally, centralized server architecture does not scale well. In order to provide better Quality of Service (QoS), service architecture such as Grid computing can be used. This type of architecture offers service scalability by utilizing heterogeneous hardware resources. In this thesis, a novel design of Grid computing middleware, Massively Multi-user Online Platform (MMOP), which integrates the Peer-to-Peer (P2P) structured overlays, is proposed. The objectives of this proposed design are to offer scalability and system design flexibility, simplify development processes of distributed applications, and improve QoS by following specified policy rules. A Massively Multiplayer Online Game (MMOG) has been created to validate the functionality and performance of MMOP. The simulation results have demonstrated that MMOP is a high performance and scalable servicing and computing middleware.

scholarly journals The effect of Covid-19 on the economy of Bangka-Belitung and the performance of MSME and its impact on competitive strategies in the new normal era

2020 ◽  
Vol 1 (1) ◽  
pp. 51-63
Author(s):  
Reniati Reniati ◽  
◽  
Muhammad Faisal Akbar ◽  
Nur Ahmad Ricky Rudianto ◽  
◽  
...  
Keyword(s):  
Negative Impact ◽  
Positive Impact ◽  
Raw Materials ◽  
Economic Losses ◽  
Change Strategies ◽  
Competitive Strategies ◽  
Online Application ◽  
The Face ◽  
Cross Tabulation ◽  
And Performance

Purpose: The economy in the MSME sector is experiencing a great crisis in the face of the Covid-19 pandemic. MSME actors must change strategies to adapt to the current pandemic conditions. The purpose of this study was to examine the effects of Covid 19 on the economy and performance of MSMEs as well as the strategies adopted by MSME players in Bangka Belitung Province. Research methodology: The data collection method employed probability sampling with google form media analyzed by using cross tabulation, trend analysis and simple mathematical modeling in projecting total economic losses to MSMEs that are active in the Bangka Belitung Island Province. Results: The results show that 84.7 percent of MSMEs experienced a negative impact due to the Covid 19 pandemic in the form of a decrease in sales turnover, reduced demand and hampered distribution of raw materials. However, 15.3 percent of MSMEs experienced a positive impact and survived in the Covid 19 pandemic conditions. Some of the strategies implemented by MSMEs to survive in the Covid 19 periods were replacing products sold with products for handling COVID 19 such as masks, hand sanitizers and others. MSME players also changed their sales strategy by selling online and maintaining customer databases. Limitation: Due to the Covid-19 condition, survey activities were carried out by using the Google Form online application. Contribution: MSMEs undertook a more aggressive marketing strategy to maintain turnover value in order to keep growing. Keywords: Covid-19, MSME, Turnover, Competitive strategy

scholarly journals Revisiting the Relationship between Military Expenditure and Economic Growth in Pakistan

2017 ◽  
Vol II (I) ◽  
pp. 18-46
Author(s):  
Waqar Qureshi ◽  
Noor Pio Khan
Keyword(s):  
Economic Growth ◽  
Transition Probabilities ◽  
Empirical Test ◽  
Transition Probability ◽  
Probability Model ◽  
Growth Period ◽  
Military Expenditure ◽  
Time Varying ◽  
Indicator Variable ◽  
Military Expenditures

This study aims to examine relationship of military expenditure and economic growth in different phases of military regimes in the context of Pakistan. This study uses two-state Markov switching models with Constant Transition Probability (CTP) and Time Varying Transition Probabilities (TVTP) for the time period: 1973-2014. This investigation analyses two sorts of relations between military expenditures and economic development through fixed transition probability Markov exchanging models. To begin with, there is negative connection between GDP growth and military expenditures during a high variance state (i.e. having low economic growth). Second, there is positive relation between both variables, during low variance state (i.e. having higher economic growth) which is also supported by idea of Keynesian income multiplier. Another, empirical test of time varying transition probability model was used to capture the switch through indicator variable. Results of the study suggest that chances of switching are increased from low to high economic growth. The chances of switching increase from lower to higher economic growth period (or high variance period) if non-military expenditure increases. The study concludes that military expenditure and economic growth are state dependent. If conditions of economy are stable then increase of expenditure results in positive outcomes, otherwise, it affects negatively. Empirical findings suggest that military spending should be planned in accordance to the economic performance of the country.

scholarly journals Updating Bridge Deck Condition Transition Probabilities as New Inspection Data Are Collected: Methodology and Empirical Evaluation

2018 ◽  
Vol 2672 (12) ◽  
pp. 93-102 ◽  
Author(s):  
Rabi G. Mishalani ◽  
Abdollah Shafieezadeh ◽  
Zequn Li
Keyword(s):  
Bridge Deck ◽  
Transition Probabilities ◽  
Empirical Evaluation ◽  
Likelihood Estimation ◽  
Bayesian Updating ◽  
Estimation Methods ◽  
Training Dataset ◽  
Original Dataset ◽  
Inspection Data ◽  
Condition Assessments

A Bayesian updating method is proposed to estimate a Markov chain based concrete deck deterioration model in a manner that combines condition data collected over two consecutive inspections and the deterioration information available prior to the collection of these data. A dataset of bridge deck condition assessments based on AASHTO condition state definitions collected by a state infrastructure agency spanning two years is used to evaluate the performance of this method. Training and validation datasets are selected from the original dataset where the former is used for estimation and the latter for prediction and evaluation. Single period transition probabilities are estimated using Bayesian updating, where prior deterioration information is combined with the condition data, and maximum likelihood estimation where only the collected condition data over two consecutive inspections are used. The evaluation is based on measuring the degree of similarity between reported condition states and those predicted based on the estimated transition probabilities using the two estimation methods. While updating transition probabilities as new data are collected is found to be advantageous for many cases, this advantage is highly dependent on the extent to which the training dataset is representative of the deterioration nature of the bridge decks for which condition is to be predicted. The less representative the training dataset, the more value is derived from Bayesian updating based predictions where prior deterioration information is considered.

scholarly journals An Efficient Document Skew Detection Method Using Probability Model and Q Test

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 55
Author(s):  
Kai Huang ◽  
Zixuan Chen ◽  
Min Yu ◽  
Xiaolang Yan ◽  
Aiguo Yin
Keyword(s):  
High Speed ◽  
Probability Model ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Connected Components ◽  
Skew Angle ◽  
Skew Detection ◽  
Detection Approach ◽  
And Performance ◽  
Bounding Boxes

Document skew detection is one of the key technologies in most of the document analysis systems. However, existing skew detection methods either have low accuracy or require a large amount of computation. To achieve a good tradeoff between efficiency and performance, we propose a novel skew detection approach based on bounding boxes, probability model, and Dixon’s Q test. Firstly, bounding boxes are used to pick out the eligible connected components (ECC). Then, we calculate the slopes of the skew document with the probability model. Finally, we find the optimal result with Dixon’s Q test and projection profile method. Moreover, the proposed method can detect the skew angle in a wider range. The experimental results show that our skew detection algorithm can achieve high speed and accuracy simultaneously compared with existing algorithms.

scholarly journals Accuracy and performance of the WHO 2015 and WHO 2019 HIV testing strategies across epidemic settings

2021 ◽  
Author(s):  
Jeffrey W Eaton ◽  
Anita Sands ◽  
Magdalena Barr-DiChiara ◽  
Muhammad S Jamil ◽  
Rachel Baggaley ◽  
...  
Keyword(s):  
Incremental Cost ◽  
Hiv Testing ◽  
Predictive Value ◽  
Probability Model ◽  
False Negative ◽  
Testing Strategy ◽  
Test Strategy ◽  
Testing Strategies ◽  
High Prevalence ◽  
And Performance

AbstractBackgroundWHO 2019 HIV testing guidelines recommended a standard HIV testing strategy consisting of three consecutively HIV-reactive test results on serology assays to diagnose HIV infection. National HIV programmes in high prevalence settings currently using the strategy consisting of only two consecutive HIV-reactive tests should consider when to implement the new guideline recommendations.Methods and FindingsWe implemented a probability model to simulate outcomes of WHO 2019 and the two strategies recommended by WHO 2015 guidelines on HIV testing services. Each assay in the strategy was assumed independently 99% sensitivity and 98% specificity, the minimal thresholds required for WHO prequalification. For each strategy and positivity ranging 20% to 0.2%, we calculated the number of false-negative, false-positive, and inconclusive results; positive and negative predictive value (PPV, NPV); number of each assay used, and testing programme costs. We found that the NPV was above 99.9% for all scenarios modelled. Under the WHO 2015 two-test strategy, the PPV was below the 99% target threshold when positivity fell below 5%. For the WHO 2019 strategy, the PPV was above 99% for all positivity levels. The number reported ‘inconclusive’ was higher under the WHO 2019 strategy. Implementing the WHO 2019 testing strategy in Malawi, would require around 89,000 A3 tests in 2021, compared to 175,000 A2 tests and over 4.5 million A1 tests per year. The incremental cost of the WHO 2019 strategy was less than 2% in 2021 and declined to 0.9% in 2025.ConclusionsAs positivity among persons testing for HIV reduces below 5% in nearly all settings, implementation of the WHO 2019 testing strategy will ensure that positive predictive value remains above the 99% target threshold, averting misdiagnoses and ART initiations among HIV uninfected people. The incremental cost of implementing the WHO 2019 HIV testing strategy compared to the two-test strategy is negligible because the third assay accounts for a small and diminishing share of total HIV tests.

Quantum Transition Probabilities and the Level of Consciousness

2010 ◽  
Vol 24 (2) ◽  
pp. 136-140 ◽  
Author(s):  
Jordan Pop-Jordanov ◽  
Nada Pop-Jordanova
Keyword(s):  
Transition Probabilities ◽  
Transition Probability ◽  
Probability Model ◽  
Analytical Formula ◽  
Characteristic Parameter ◽  
Dipole Interaction ◽  
Quantum Transition ◽  
Frequency Interval ◽  
Consciousness Level ◽  
Level Of Consciousness

This paper proposes a theoretical approach to explain the characteristic empirical interdependence between the states of arousal (representing the level of consciousness) and EEG activity. Applying a quantum transition probability model to the brain’s electric field-dipole interaction, an analytical formula is derived that corresponds to the empirical arousal-frequency correlation, both in form (sigmoid) and the frequency interval (from 0 to 100 Hz). In addition, we consider the possible theoretical and practical implications. Thus, a general formula for consciousness level is deduced, with equilibrium frequency as the characteristic parameter. We also briefly discuss the quantum-classical coupling and nested relationship between the consciousness level and content as well as the clinical relevance.

An adaptive approach for estimation of transition probability matrix in the interacting multiple model filter

2021 ◽  
pp. 1-12
Author(s):  
Luciana Balieiro Cosme ◽  
Marcos Flávio Silveira Vasconcelos D’Angelo ◽  
Walmir Matos Caminhas ◽  
Murilo Osorio Camargos ◽  
Reinaldo Martínez Palhares
Keyword(s):  
Transition Probabilities ◽  
Transition Probability ◽  
Computational Cost ◽  
Transition Probability Matrix ◽  
World Health ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Data Set ◽  
Retention Models ◽  
System States

The traditional Interacting Multiple Model (IMM) filters usually consider that the Transition Probability Matrix (TPM) is known, however, when the IMM is associated with time-varying or inaccurate transition probabilities the estimation of system states may not be predicted adequately. The main methodological contribution of this paper is an approach based on the IMM filter and retention models to determine the TPM adaptively and automatically with relatively low computational cost and no need for complex operations or storing the measurement history. The proposed method is compared to the traditional IMM filter, IMM with Bayesian Network (BNs) and a state-of-the-art Adaptive TPM-based parallel IMM (ATPM-PIMM) algorithm. The experiments were carried out in an artificial numerical example as well as in two real-world health monitoring applications: the PRONOSTIA platform and the Li-ion batteries data set provided by NASA. The Retention Interacting Multiple Model (R-IMM) results indicate that a better prediction performance can be obtained when the TPM is not properly adjusted or not precisely known.

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